Responsible AI Pattern Catalogue
Artificial Intelligence (AI) has been transforming our society and listed as the top strategic technology in many organizations. Although AI has huge potential to solve real-world challenges, there are serious concerns about its ability to behave and make decisions in a responsible way. Compared to traditional software systems, AI systems involve higher degree of uncertainty and more ethical risk due to its autonomous and opaque decision making. Responsible AI is the practice of developing and using AI systems in a way that benefits to individuals, groups, and the wider society, while minimizing the risk of negative consequences.
The concept of responsible AI has attracted huge attention from governments, organizations, and companies. According to the 2022 Gartner CIO and Technology Executive Survey, 48% organizations have already adopted or plan to adopt AI technologies within the next 12 months while 21% of organizations have already deployed or plan to deploy responsible AI technologies within the next 12 months. Responsible AI has been widely considered as one of the greatest scientific challenges of our time and the key to unlock the market and increase the adoption of AI.
To address the responsible AI challenge, a number of AI ethics principles frameworks (e.g., Australia’s AI Ethics Principles) have been published recently, which AI systems are supposed to conform to. There have been a consensus around the AI ethics principles. A principle-based approach allows technology-neutral, future-proof and context-specific interpretations and operationalization. However, without further best practice guidance, practitioners are left with nothing much beyond truisms. For example, it is a very challenging and complex task to operationalize the the human-centered value principle regarding how it can be designed for, implemented and monitored throughout the entire lifecycle of AI systems. In addition, significant efforts have been put on algorithm-level solutions which mainly focus on a subset of mathematics-amenable ethical principles (such as privacy and fairness). However, ethical issues can occur at any step of the development lifecycle crosscutting many AI, non-AI and data components of systems beyond AI algorithms and models. To try to fill the principle-algorithm gap, further guidance such as guidebooks, questions to generate discussions, checklists and documentation templates do start to appear. Those efforts tend to be ad-hoc sets of more detailed prompts for practitioners to think about all the issues and come up with their own solutions.
Overview of Responsible AI Pattern Catalogue
We adopt a pattern-oriented approach and build up a Responsible AI Pattern Catalogue, as illustrated in Figure 1, for operationalizing responsible AI from a system perspective. In software engineering, a pattern is a reusable solution to a problem commonly occurring within a given context in software development. Many solutions contribute to multiple responsible AI principles. Rather than staying at the ethical principle or algorithm level, we focus on patterns that practitioners and broader stakeholders can undertake to ensure that responsible AI systems are responsibly developed throughout the entire lifecycle with different levels of governance. To describe the pattern, we created a template, including summary, type, objective, target users, impacted stakeholders, relevant principles, context, problem, solution, consequences, related patterns, known uses. The approach has the following characteristics.
- Across multiple organization levels and connected – industry/community, organization, and teams. The patterns we are introducing here are at different levels, so one can situate your practice areas in the bigger picture and see how you fit in and how different practices and patterns influence and reinforce each other from a team, organization, and industry/community level.
- Across multiple angles – governance, process, and product. Not only should you use product patterns to enforce responsible AI principles directly in the product and verify/validate the product, but you should also use process and governance patterns to complement it further:
- Governance patterns for establishing multi-level governance for responsible AI;
- Process patterns for setting up trustworthy development processes;
- Product patterns for building responsible-AI-by-design into AI systems.
- Across system life cycle and connected – requirements, design, implementation, testing, deployment, and post-deployment monitoring. Across the life cycle of AI systems, different patterns and practices can be applied at other times, with the outputs of one practice becoming the input of another.
- Across the supply chain, system, and operation layer and connected. We connect most of the patterns through a system reference architecture across AI supply chain, AI system, and operation/deployment infrastructure layer.
- Benefiting multiple connected risks. Individual responsible AI risks should be managed in silos by using risk-specific solutions. The patterns in this catalogue often help multiple risks together to raise the responsible AI posture of the organization significantly.
- Acknowledging drawbacks and additional risks introduced. Adopting risk mitigation may introduce additional risks and costs. We recognize them by incorporating drawbacks in the patterns and connecting with other related ways to tackle the challenges further.
- Clear differentiation of trust and trustworthiness. We recognize that the importance of gaining stakeholder trust goes beyond the objective trustworthiness of the systems. Gaining trust is about diverse and inclusive engagement, setting realistic expectations, and communicating trustworthiness evidence in a way that stakeholders can understand and meaningfully critique. We include trust and trustworthiness dimensions in our patterns.
We hope these multi-layer, multi-aspect, multi-stage, and connected patterns can help you better navigate the landscape and achieve responsible AI systems more successfully.
AI System Stakeholders
As illustrated in Fig. 2, AI system stakeholders are classified into three groups:
- Industry-level stakeholders
- AI technology producers: those who develop AI technologies for others to build on top to produce AI solutions, e.g., parts of Google, Microsoft, IBM. AI technology producers may embed RAI in their technologies and/or provide additional RAI tools.
- AI technology procurers: those who procure AI technologies to build their in-house AI solutions, e.g., companies or government agencies buying/using AI platform/tools. AI technology procurers may care about RAI issues and embed RAI into their AI technology procurement process.
- AI solution producers: those who develop in-house/blended unique solutions on top of technology solutions and need to make sure the solutions adhere to RAI principles/standards/regulations, e.g., parts of MS/Google providing Office/Gmail “solutions”. They may offer the solutions to AI consumers directly or sell to others. They may use RAI tools (provided by tech producers or 3rd parties) and RAI processes during their solution development.
- AI solution procurers: those who procure complete AI solutions (with some further configuration and instantiation) to use internally or offer to external AI consumers, e.g., a government agency buying from a complete solution from vendors. They may care about RAI issues and embed RAI into their AI solution procurement process.
- AI users: those who use an AI solution to make decisions that may impact on a subject, e.g., a loan officer or a gov employee. AI users may exercise additional RAI oversight as the human-in-the-loop.
- AI impacted subjects: those who are impacted by some AI-human dyad decisions, e.g., a loan applicant or a tax payer. AI impacted subjects may contest the decision on dyad AI ground.
- AI consumers: those who consume AI solutions (e.g., voice assistants, search engines, recommender engines) for their personal use (not affecting 3rd parties). AI consumers may care about the dyad AI aspects of AI solutions.
- RAI governors: those who set and enable RAI policies and controls within their culture. RAI governors could be functions within an organization in the above list or external (regulators, consumer advocacy groups, community).
- RAI tool producers: those who are technology vendors and dedicated companies offering RAI features integrated into AI platforms or AIOps/MLOps tools.
- RAI tool procurers: any of the above stakeholders who may purchase or use RAI tools to improve or check solutions/technology’s RAI aspects.
- Organization-level stakeholders
- Management teams: individuals at the higher level of an organization who are responsible for establishing RAI governance structure in the organization and achieving RAI at the organization-level. The management teams include board members, executives, and (middle-level) managers for legal, compliance, privacy, security, risk, and sustainability.
- Employees: individuals who are hired by an organization to perform work for the organization and expected to adhere to RAI principles in their work.
- Team-level stakeholders
- Development teams: those who are responsible for developing and deploying AI systems, including product managers, project managers, team leaders, business analysts, architects, UX/UI designers, data scientists, developers, testers, and operators. The development teams are expected to implement RAI in their development process and embed RAI into the product design of AI systems.
We identify a set of governance patterns and classify them into industry-level governance patterns, organization-level governance patterns, and team-level governance patterns (see Fig.3). The target users of industry-level governance patterns are RAI governors, while the impacted stakeholders include AI technology producers and procurers, AI solution producers and procurers, RAI tool providers and procurers. For the organization-level patterns, the target users are the management team and the impacted stakeholders are employees. The target users of team-level patterns are the development team.
Industry-level governance patterns
- AI regulation
- RAI maturity model
- RAI certification
- Regulatory sandbox
- Building code
- Independent oversight
- Trust mark
- RAI standard
Organization-level governance patterns
- Leadership commitment for RAI
- RAI risk committee
- Code for RAI
- RAI risk assessment
- RAI training
- Role-level accountability contract
- RAI bill of materials
- Standardized reporting
Team-level governance patterns
- Customized agile process
- Tight coupling of AI and non-AI development
- Diverse team
- Stakeholder engagement
- Continuous documentation using templates
- Verifiable claim for AI system artifacts
- Failure mode and effects analysis (FMEA)
- Fault tree analysis (FTA)
We identify process-oriented patterns (i.e. best practices) that can be incorporated into development processes, so the developers could consider to apply them during the development lifecycle. Fig.4 describes the software development lifecycle and the potential ethical risks and breaches corresponding to each stage, while Fig.5 presents the summarized patterns for different stages.
Patterns for requirement stage
- AI suitability assessment
- Verifiable RAI requirement
- Lifecycle-driven data requirement
- RAI user story
Patterns for design stage
- Multi-level co-architecting
- Envisioning card
- RAI design modelling
- System-level RAI simulation
- XAI interface
Patterns for implementation stage
Patterns for testing stage
Patterns for operation stage
- Continuous deployment for RAI
- Extensible, adaptive and dynamic RAI risk assessment
- Multi-level co-versioning
Product patterns provide a system-level guidance on how to design the architecture of responsible AI systems. Responsible-AI-by-design can be built into AI systems through the product patterns. Broadly, an AI system is comprised by three layers, including the supply chain layer that generates the software components which compose the AI system, the system layer which is deployed AI system, and the operation infrastructure layer that provides auxiliary functions to the AI system. Fig.6 presents the identified products patterns for each of the three layers. Those product patterns can be embedded into the AI ecosystems as product features. Fig.7 illustrates a state diagram of a provisioned AI system and highlights the patterns associating with relevant states or transitions, which show when the product patterns could take effect. Fig.8 gives a pattern-oriented responsible-AI-by-design reference architecture.
Supply chain patterns
Operation infrastructure patterns
- Continuous RAI validator
- RAI sandbox
- RAI knowledge base
- RAI digital twin
- Incentive registry
- RAI black box
- Global view auditor
HAI interaction patterns
Responsible AI Pattern Catalogue and Question Bank:
- Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Didar Zowghi, Aurelie Jacquet, Responsible AI Pattern Catalogue: A Multivocal Literature Review. arXiv preprint arXiv:2209.04963, 2022.
- Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems. IEEE Software, 2023.
- Qinghua Lu, Yuxiu Luo, Liming Zhu, Mingjian Tang, Xiwei Xu, Jon Whittle. Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach. IEEE Intelligent Systems, 2023.
- Boming Xia, Tingting Bi, Zhenchang Xing, Qinghua Lu, Liming Zhu. An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE’2023).
- Boming Xia, Qinghua Lu, Harsha Perera, Liming Zhu, Zhenchang Xing, Yue Liu, Jon Whittle. Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study. 2023 ACM/IEEE 2nd International Conference on AI Engineering (CAIN’2023).
- Sung Une Lee, Harsha Perera, Boming Xia, Yue Liu, Qinghua Lu, Liming Zhu, Olivier Salvado, Jon Whittle. QB4AIRA: A Question Bank for AI Risk Assessment. arXiv preprint arXiv:2305.09300, 2023
Software Engineering for Responsible AI:
- Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing, Towards a Roadmap on Software Engineering for Responsible AI. 2022 ACM/IEEE 1st International Conference on AI Engineering (CAIN’2022). ACM SIGSOFT Distinguished Paper Award.
- Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, David Douglas, Conrad Sanderson. Software engineering for responsible AI: An empirical study and operationalised patterns. 2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).
Architecture Design for Foundation Models based AI Systems:
- Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Jon Whittle. Towards Responsible AI in the Era of ChatGPT: A Reference Architecture for Designing Foundation Model-based AI Systems. arXiv preprint arXiv:2304.11090, 2023.
- Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Jon Whittle. A Framework for Designing Foundation Model based Systems. arXiv preprint arXiv:2305.05352, 2023.
Qinghua Lu: firstname.lastname@example.org